Discovery of Ranking Fraud
for Mobile Apps
Abstract:
Ranking
fraud in the mobile App market refers to fraudulent or deceptive activities
which have a purpose of bumping up the Apps in the popularity list. Indeed, it
becomes more and more frequent for App developers to use shady means, such as
inflating their Apps’ sales or posting phony App ratings, to commit ranking
fraud. While the importance of preventing ranking fraud has been widely recognized,
there is limited understanding and research in this area. To this end, in this
paper, we provide a holistic view of ranking fraud and propose a ranking fraud
detection system for mobile Apps. Specifically, we first propose to accurately
locate the ranking fraud by mining the active periods, namely leading sessions,
of mobile Apps. Such leading sessions can be leveraged for detecting the local anomaly
instead of global anomaly of App rankings. Furthermore, we investigate three
types of evidences, i.e., ranking based evidences, rating based evidences and
review based evidences, by modeling Apps’ ranking, rating and review behaviors
through statistical hypotheses tests. In addition, we propose an optimization
based aggregation method to integrate all the evidences for fraud detection.
Introduction:
In a recent
trend, instead of relying on traditional marketing solutions, shady App
developers resort to some fraudulent means to deliberately boost their Apps and
eventually manipulate the chart rankings on an App store. This is usually
implemented by using so-called “bot farms” or “human water armies” to inflate
the App downloads, ratings and reviews in a very short time. Indeed, our
careful observation reveals that mobile Apps are not always ranked high in the
leader board, but only in some leading events, which form different leading
sessions. Note that we will introduce both leading events and leading sessions
in detail later. In other words, ranking fraud usually happens in these leading
sessions. Therefore, detecting ranking fraud of mobile Apps is actually to
detect ranking fraud within leading sessions of mobile Apps.
Existing System:
The
analysis of Apps’ ranking behaviors, we find that the fraudulent Apps often
have different ranking patterns in each leading session compared with normal Apps.
Thus, we characterize some fraud evidences from Apps’ historical ranking
records, and develop three functions to extract such ranking based fraud
evidences.
Nonetheless, the ranking
based evidences can be affected by App developers’ reputation and some
legitimate marketing campaigns, such as “limited-time discount”. As a result,
it is not sufficient to only use ranking based evidences.
Disadvantages:
·
In existing framework the
leading session evidences are collude with duplicate evidences.
·
To extract the rating
solution consumes lot of time as collection of leading session data.
Proposed System:
To
extract and combine fraud evidences for ranking fraud detection by ranking
based evidences, rating based evidences and review based evidences. To study
the performance of ranking fraud detection by each approach, we set up the
evaluation as follows. First, for each approach, we selected 50 top ranked
leading sessions (i.e., most suspicious sessions), 50 middle ranked leading
sessions (i.e., most uncertain sessions), and 50 bottom ranked leading sessions
(i.e., most normal sessions) from each data set. Then, we merged all the
selected sessions into a pool which consists 587 unique sessions from 281
unique Apps in “Top Free 300” data set, and 541 unique sessions from 213 unique
Apps in “Top Paid 300” data set. Second, we invited five human evaluators who
are familiar with Apple’s App store and
mobile Apps to manually label the selected leading sessions with score 2 (i.e.,
Fraud), 1 (i.e., Not Sure) and 0 (i.e., Non-fraud). Specifically, for each selected
leading session, each evaluator gave a proper score by comprehensively
considering the profile information of the App (e.g., descriptions,
screenshots), the trend of rankings during this session, the App leader board
information during this session, the trend of ratings during this session, and
the reviews during this session.
Advantages:
ü Data redundancy is removed at each session of proposed framework
session.
ü Observation results are stored securely.
Software Requriments
Front End: HTML5, CSS3, Bootstrap
Back End: PHP, MYSQL
Control End: Angular Java Script
Tool:
Android SDK, Xampp, Eclipse
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Android Project Titles 2017-2018
Android Project Titles 2017-2018
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